Next Article in Journal
Heavy-Ion Induced Single Event Upsets in Advanced 65 nm Radiation Hardened FPGAs
Previous Article in Journal
Improved Fractional Open Circuit Voltage MPPT Methods for PV Systems
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Electronics 2019, 8(3), 322; https://doi.org/10.3390/electronics8030322

Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection

1
Department of Computer Science & Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
2
Department of Biomedical Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
*
Author to whom correspondence should be addressed.
Received: 11 February 2019 / Revised: 4 March 2019 / Accepted: 11 March 2019 / Published: 14 March 2019
(This article belongs to the Section Computer Science & Engineering)
Full-Text   |   PDF [3768 KB, uploaded 14 March 2019]   |  
  |   Review Reports

Abstract

The security of networked systems has become a critical universal issue that influences individuals, enterprises and governments. The rate of attacks against networked systems has increased dramatically, and the tactics used by the attackers are continuing to evolve. Intrusion detection is one of the solutions against these attacks. A common and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. The performance of an IDS is significantly improved when the features are more discriminative and representative. This study uses two feature dimensionality reduction approaches: (i) Auto-Encoder (AE): an instance of deep learning, for dimensionality reduction, and (ii) Principle Component Analysis (PCA). The resulting low-dimensional features from both techniques are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. This research effort is able to reduce the CICIDS2017 dataset’s feature dimensions from 81 to 10, while maintaining a high accuracy of 99.6% in multi-class and binary classification. Furthermore, in this paper, we propose a Multi-Class Combined performance metric C o m b i n e d M c with respect to class distribution to compare various multi-class and binary classification systems through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset. View Full-Text
Keywords: Dimensionality Reduction; Intrusion Detection System (IDS); Sparse Auto Encoder (SAE); Principle Component Analysis (PCA); Uniform Distribution Based Balancing (UDBB) Dimensionality Reduction; Intrusion Detection System (IDS); Sparse Auto Encoder (SAE); Principle Component Analysis (PCA); Uniform Distribution Based Balancing (UDBB)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Abdulhammed, R.; Musafer, H.; Alessa, A.; Faezipour, M.; Abuzneid, A. Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection. Electronics 2019, 8, 322.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top